Research On Power Flow Optimization Method Of Microgrid

Due to the non-renewability of traditional energy sources such as coal and oil,and the environmental problems brought about by people’s increasing attention to traditional energy sources,countries are stepping up research on the development and utilization of renewable energy sources.Power generation systems are also developing rapidly.Although more and more microgrids are integrated into the power grid,although the shortcomings of energy dispersion and small capacity cannot be effectively generated,the characteristics of new energy represented by photovoltaic and wind power determine that the power flow optimization of microgrids faces new challenges..From the perspective of optimal economic and environmental protection in microgrid power flow optimization,this paper selects particle swarm and NSGA-II algorithms as the research objects,discusses the basic theories of the two algorithms and improves them according to their shortcomings.The feasibility and superiority of the improvement are verified in an example.The following are the primary research topics for this topic:(1)The basic theory of distributed power generation and energy storage system in microgrid is discussed,and their respective mathematical models are established.Among them,distributed power sources are divided into schedulable power sources and non-schedulable power sources.The unschedulable ones include photovoltaics and wind power,and the schedulable ones include gas turbines,diesel generators and fuel cells.These contents provide the basic model for the microgrid optimization that follows.(2)The basic concepts and principles of particle swarm optimization and NSGA-II algorithm are introduced.These two algorithms are widely used in microgrid optimization,but particle swarm optimization will lead to slower convergence speed under the influence of microgrid data,and will fall into the local optimal solution;while the NSGA-II algorithm solves the problem that the genetic algorithm is easy to fall into the local optimal solution and the NSGA algorithm has a slow convergence speed and is susceptible to human subjective influence,but it still has some shortcomings in the convergence accuracy and calculation amount.In this paper,the two algorithms are improved,for the particle swarm algorithm,the inertia weight and learning factor have been improved.The improved particle swarm can break free from the local optimum,improving the optimization effect.The improvement of the NSGA-II algorithm is to add reverse learning to the algorithm,so that the optimization effect of the NSGA-II algorithm is better.(3)The multi-objective function models of single microgrid and multi microgrid are established respectively,and the corresponding optimal dispatching strategies are set for the two models.The objective function of single microgrid is economic cost and environmental cost,and the algorithm is improved particle swarm optimization algorithm;The objective function of multi microgrid is also economic cost and environmental cost.The algorithm is improved NSGA-Ⅱ algorithm.Finally,through simulation analysis,the example is verified and analyzed.The results verify the good effects of the two improved algorithms in microgrid power flow optimization.